Abstract
In this paper, the implementations of two reinforcement learnings namely, Q learning and deep Q network (DQN) on the Gazebo model of a self balancing robot have been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The more time it can remain within a specified limit, the more reward it accumulates and hence more balanced it is. We did various tests with many hyperparameters and demonstrated the performance curves.
Highlights
Control system is one of the most critical aspects of Robotics Research
“Reinforcement learning methods as controllers” section shows the implementation of Q Learning and deep Q network (DQN) as controllers
Comparison to traditional methods In our previous paper, [1], we evaluated the performance of PID, Fuzzy Logic and LQR on a self-balancing robot model and compared among those controllers
Summary
Control system is one of the most critical aspects of Robotics Research. The Gazebo is one of the most robust multi-robot simulators at present. In our previous paper, [1], we attempted to demonstrate and document the use of PID, Fuzzy logic and LQR controllers using ROS and Gazebo on a selfbalancing robot model. Derivation of Q values in one forward pass In the classical Q learning approach, one has to give state and action as an input resulting in Q value for that state and action Replicating this approach in Neural Network is problematic as one has to give state and action for each possible action of the agent to the Model (Additional file 7). Comparison to traditional methods In our previous paper, [1], we evaluated the performance of PID, Fuzzy Logic and LQR on a self-balancing robot model and compared among those controllers. Narrowing down that range will help the architecture to perform better (Additional file 8)
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